Overview

Dataset statistics

Number of variables31
Number of observations25139
Missing cells4141
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 MiB
Average record size in memory248.0 B

Variable types

Numeric22
Text5
Categorical4

Alerts

Low_Threshold_Type has constant value "SNAP"Constant
Annual_Food_Budget_Shortfall is highly overall correlated with HOUSEHOLDS_SNAP and 7 other fieldsHigh correlation
Cost_Per_Meal is highly overall correlated with YEARHigh correlation
FIPS is highly overall correlated with STATE_FIPSHigh correlation
Food_Insecurity_Rate is highly overall correlated with MEDIAN_HOUSEHOLD_INCOME and 3 other fieldsHigh correlation
HOUSEHOLDS_SNAP is highly overall correlated with Annual_Food_Budget_Shortfall and 7 other fieldsHigh correlation
HOUSEHOLDS_TOTAL is highly overall correlated with Annual_Food_Budget_Shortfall and 7 other fieldsHigh correlation
High_Threshold_State is highly overall correlated with High_Threshold_Type and 2 other fieldsHigh correlation
High_Threshold_Type is highly overall correlated with High_Threshold_State and 3 other fieldsHigh correlation
Low_Threshold_State is highly overall correlated with High_Threshold_State and 1 other fieldsHigh correlation
MEDIAN_HOUSEHOLD_INCOME is highly overall correlated with Food_Insecurity_Rate and 4 other fieldsHigh correlation
Num_Food_Insecure_Children is highly overall correlated with Annual_Food_Budget_Shortfall and 7 other fieldsHigh correlation
Num_Food_Insecure_Persons is highly overall correlated with Annual_Food_Budget_Shortfall and 7 other fieldsHigh correlation
POP_16_PLUS is highly overall correlated with Annual_Food_Budget_Shortfall and 7 other fieldsHigh correlation
POP_BELOW_POVERTY is highly overall correlated with Annual_Food_Budget_Shortfall and 7 other fieldsHigh correlation
POP_POVERTY_DETERMINED is highly overall correlated with Annual_Food_Budget_Shortfall and 7 other fieldsHigh correlation
POP_UNEMPLOYED is highly overall correlated with Annual_Food_Budget_Shortfall and 7 other fieldsHigh correlation
POVERTY_RATE is highly overall correlated with Food_Insecurity_Rate and 5 other fieldsHigh correlation
Pct_FI_Above_High_Threshold is highly overall correlated with MEDIAN_HOUSEHOLD_INCOME and 3 other fieldsHigh correlation
Pct_FI_Below_Low_Threshold is highly overall correlated with POVERTY_RATE and 2 other fieldsHigh correlation
Pct_FI_Between_Thresholds is highly overall correlated with High_Threshold_TypeHigh correlation
SNAP_RECEIPT_RATE is highly overall correlated with Food_Insecurity_Rate and 5 other fieldsHigh correlation
STATE_FIPS is highly overall correlated with FIPS and 2 other fieldsHigh correlation
UNEMPLOYMENT_RATE is highly overall correlated with Food_Insecurity_Rate and 3 other fieldsHigh correlation
YEAR is highly overall correlated with Cost_Per_MealHigh correlation
Pct_FI_Between_Thresholds has 4133 (16.4%) missing valuesMissing
Pct_FI_Between_Thresholds has 2273 (9.0%) zerosZeros
Pct_FI_Above_High_Threshold has 563 (2.2%) zerosZeros

Reproduction

Analysis started2025-12-08 17:04:00.835322
Analysis finished2025-12-08 17:04:31.819079
Duration30.98 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

YEAR
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.4997
Minimum2011
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:31.845041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12012
median2014
Q32016
95-th percentile2018
Maximum2018
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2913768
Coefficient of variation (CV)0.0011374421
Kurtosis-1.2381663
Mean2014.4997
Median Absolute Deviation (MAD)2
Skewness0.00018602502
Sum50642508
Variance5.2504077
MonotonicityIncreasing
2025-12-08T12:04:31.894263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
20113143
12.5%
20123143
12.5%
20133143
12.5%
20143142
12.5%
20153142
12.5%
20163142
12.5%
20173142
12.5%
20183142
12.5%
ValueCountFrequency (%)
20113143
12.5%
20123143
12.5%
20133143
12.5%
20143142
12.5%
20153142
12.5%
20163142
12.5%
20173142
12.5%
20183142
12.5%
ValueCountFrequency (%)
20183142
12.5%
20173142
12.5%
20163142
12.5%
20153142
12.5%
20143142
12.5%
20133143
12.5%
20123143
12.5%
20113143
12.5%

FIPS
Real number (ℝ)

High correlation 

Distinct3145
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30386.191
Minimum1001
Maximum56045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:31.957002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile5093
Q118177
median29177
Q345081
95-th percentile53063
Maximum56045
Range55044
Interquartile range (IQR)26904

Descriptive statistics

Standard deviation15161.218
Coefficient of variation (CV)0.49895094
Kurtosis-1.0983607
Mean30386.191
Median Absolute Deviation (MAD)12022
Skewness-0.079773286
Sum7.6387844 × 108
Variance2.2986254 × 108
MonotonicityNot monotonic
2025-12-08T12:04:32.032899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
371878
 
< 0.1%
370438
 
< 0.1%
370518
 
< 0.1%
370818
 
< 0.1%
370998
 
< 0.1%
371398
 
< 0.1%
371558
 
< 0.1%
370398
 
< 0.1%
371818
 
< 0.1%
371598
 
< 0.1%
Other values (3135)25059
99.7%
ValueCountFrequency (%)
10018
< 0.1%
10038
< 0.1%
10058
< 0.1%
10078
< 0.1%
10098
< 0.1%
10118
< 0.1%
10138
< 0.1%
10158
< 0.1%
10178
< 0.1%
10198
< 0.1%
ValueCountFrequency (%)
560458
< 0.1%
560438
< 0.1%
560418
< 0.1%
560398
< 0.1%
560378
< 0.1%
560358
< 0.1%
560338
< 0.1%
560318
< 0.1%
560298
< 0.1%
560278
< 0.1%

STATE_FIPS
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.282549
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:32.104362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q118
median29
Q345
95-th percentile53
Maximum56
Range55
Interquartile range (IQR)27

Descriptive statistics

Standard deviation15.143018
Coefficient of variation (CV)0.50005759
Kurtosis-1.0983667
Mean30.282549
Median Absolute Deviation (MAD)12
Skewness-0.081803817
Sum761273
Variance229.31101
MonotonicityNot monotonic
2025-12-08T12:04:32.174277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
482032
 
8.1%
131272
 
5.1%
511067
 
4.2%
21960
 
3.8%
29920
 
3.7%
20840
 
3.3%
17816
 
3.2%
37800
 
3.2%
19792
 
3.2%
47760
 
3.0%
Other values (41)14880
59.2%
ValueCountFrequency (%)
1536
2.1%
2232
 
0.9%
4120
 
0.5%
5600
2.4%
6464
1.8%
8512
2.0%
964
 
0.3%
1024
 
0.1%
118
 
< 0.1%
12536
2.1%
ValueCountFrequency (%)
56184
 
0.7%
55576
 
2.3%
54440
 
1.8%
53312
 
1.2%
511067
4.2%
50112
 
0.4%
49232
 
0.9%
482032
8.1%
47760
 
3.0%
46528
 
2.1%

COUNTY_FIPS
Real number (ℝ)

Distinct326
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.64155
Minimum1
Maximum840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:32.344570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q135
median79
Q3133
95-th percentile299
Maximum840
Range839
Interquartile range (IQR)98

Descriptive statistics

Standard deviation107.79469
Coefficient of variation (CV)1.0400722
Kurtosis11.311196
Mean103.64155
Median Absolute Deviation (MAD)48
Skewness2.8355964
Sum2605445
Variance11619.696
MonotonicityNot monotonic
2025-12-08T12:04:32.418771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3392
 
1.6%
1392
 
1.6%
5392
 
1.6%
9384
 
1.5%
7376
 
1.5%
13376
 
1.5%
11368
 
1.5%
15368
 
1.5%
19360
 
1.4%
17360
 
1.4%
Other values (316)21371
85.0%
ValueCountFrequency (%)
1392
1.6%
3392
1.6%
5392
1.6%
68
 
< 0.1%
7376
1.5%
9384
1.5%
11368
1.5%
128
 
< 0.1%
13376
1.5%
148
 
< 0.1%
ValueCountFrequency (%)
8408
< 0.1%
8308
< 0.1%
8208
< 0.1%
8108
< 0.1%
8008
< 0.1%
7908
< 0.1%
7758
< 0.1%
7708
< 0.1%
7608
< 0.1%
7508
< 0.1%

MEDIAN_HOUSEHOLD_INCOME
Real number (ℝ)

High correlation 

Distinct18148
Distinct (%)72.2%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean47444.434
Minimum18972
Maximum136268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:32.491727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18972
5-th percentile31199.4
Q139193
median45536
Q353072
95-th percentile71227.6
Maximum136268
Range117296
Interquartile range (IQR)13879

Descriptive statistics

Standard deviation12618.287
Coefficient of variation (CV)0.26595926
Kurtosis3.4419537
Mean47444.434
Median Absolute Deviation (MAD)6877
Skewness1.3436893
Sum1.1926107 × 109
Variance1.5922116 × 108
MonotonicityNot monotonic
2025-12-08T12:04:32.568636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4875018
 
0.1%
5000013
 
0.1%
4000012
 
< 0.1%
4750012
 
< 0.1%
3875011
 
< 0.1%
3500010
 
< 0.1%
4312510
 
< 0.1%
4250010
 
< 0.1%
4500010
 
< 0.1%
325009
 
< 0.1%
Other values (18138)25022
99.5%
ValueCountFrequency (%)
189721
< 0.1%
191461
< 0.1%
192641
< 0.1%
193281
< 0.1%
193441
< 0.1%
195011
< 0.1%
196241
< 0.1%
199361
< 0.1%
199861
< 0.1%
200001
< 0.1%
ValueCountFrequency (%)
1362681
< 0.1%
1295881
< 0.1%
1256721
< 0.1%
1247961
< 0.1%
1239661
< 0.1%
1234531
< 0.1%
1228441
< 0.1%
1222381
< 0.1%
1220681
< 0.1%
1211331
< 0.1%

POP_POVERTY_DETERMINED
Real number (ℝ)

High correlation 

Distinct21631
Distinct (%)86.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean97750.092
Minimum56
Maximum9955473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:32.644929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile2779.25
Q110603.25
median24679
Q364876.75
95-th percentile424089.15
Maximum9955473
Range9955417
Interquartile range (IQR)54273.5

Descriptive statistics

Standard deviation315689.65
Coefficient of variation (CV)3.2295585
Kurtosis335.88469
Mean97750.092
Median Absolute Deviation (MAD)17727
Skewness14.192419
Sum2.4572418 × 109
Variance9.9659952 × 1010
MonotonicityNot monotonic
2025-12-08T12:04:32.719869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105556
 
< 0.1%
147405
 
< 0.1%
98945
 
< 0.1%
139095
 
< 0.1%
35905
 
< 0.1%
47095
 
< 0.1%
146364
 
< 0.1%
19064
 
< 0.1%
68764
 
< 0.1%
98664
 
< 0.1%
Other values (21621)25091
99.8%
ValueCountFrequency (%)
561
 
< 0.1%
621
 
< 0.1%
641
 
< 0.1%
661
 
< 0.1%
682
< 0.1%
701
 
< 0.1%
731
 
< 0.1%
792
< 0.1%
831
 
< 0.1%
853
< 0.1%
ValueCountFrequency (%)
99554731
< 0.1%
99477991
< 0.1%
99060131
< 0.1%
98861331
< 0.1%
98193971
< 0.1%
97383701
< 0.1%
96845031
< 0.1%
96330801
< 0.1%
51520231
< 0.1%
51516701
< 0.1%

POP_BELOW_POVERTY
Real number (ℝ)

High correlation 

Distinct13376
Distinct (%)53.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean14583.1
Minimum0
Maximum1805868
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:32.791198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile345
Q11676
median4176
Q310025.25
95-th percentile55626.1
Maximum1805868
Range1805868
Interquartile range (IQR)8349.25

Descriptive statistics

Standard deviation51872.569
Coefficient of variation (CV)3.5570329
Kurtosis416.46973
Mean14583.1
Median Absolute Deviation (MAD)3088
Skewness16.23119
Sum3.6658998 × 108
Variance2.6907634 × 109
MonotonicityNot monotonic
2025-12-08T12:04:32.865793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63412
 
< 0.1%
14011
 
< 0.1%
78211
 
< 0.1%
60610
 
< 0.1%
92310
 
< 0.1%
48110
 
< 0.1%
61410
 
< 0.1%
19709
 
< 0.1%
1629
 
< 0.1%
5059
 
< 0.1%
Other values (13366)25037
99.6%
ValueCountFrequency (%)
02
 
< 0.1%
41
 
< 0.1%
61
 
< 0.1%
73
< 0.1%
92
 
< 0.1%
107
< 0.1%
111
 
< 0.1%
124
< 0.1%
142
 
< 0.1%
161
 
< 0.1%
ValueCountFrequency (%)
18058681
< 0.1%
18002651
< 0.1%
17640811
< 0.1%
17372241
< 0.1%
16885051
< 0.1%
16582311
< 0.1%
15899561
< 0.1%
15660661
< 0.1%
8862611
< 0.1%
8794991
< 0.1%

POP_16_PLUS
Real number (ℝ)

High correlation 

Distinct21100
Distinct (%)83.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean79487.103
Minimum57
Maximum8115158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:32.940944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile2268.85
Q18789.25
median20629
Q353803.75
95-th percentile338267.95
Maximum8115158
Range8115101
Interquartile range (IQR)45014.5

Descriptive statistics

Standard deviation253666.1
Coefficient of variation (CV)3.1912863
Kurtosis338.95257
Mean79487.103
Median Absolute Deviation (MAD)14736.5
Skewness14.204083
Sum1.9981468 × 109
Variance6.4346493 × 1010
MonotonicityNot monotonic
2025-12-08T12:04:33.016105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113636
 
< 0.1%
19806
 
< 0.1%
95366
 
< 0.1%
16136
 
< 0.1%
77476
 
< 0.1%
94245
 
< 0.1%
179975
 
< 0.1%
84875
 
< 0.1%
150045
 
< 0.1%
141245
 
< 0.1%
Other values (21090)25083
99.8%
ValueCountFrequency (%)
571
< 0.1%
621
< 0.1%
661
< 0.1%
671
< 0.1%
701
< 0.1%
712
< 0.1%
721
< 0.1%
781
< 0.1%
791
< 0.1%
811
< 0.1%
ValueCountFrequency (%)
81151581
< 0.1%
81024021
< 0.1%
80360771
< 0.1%
79970891
< 0.1%
79135801
< 0.1%
78153291
< 0.1%
77370111
< 0.1%
76634841
< 0.1%
41956101
< 0.1%
41923721
< 0.1%

POP_UNEMPLOYED
Real number (ℝ)

High correlation 

Distinct17253
Distinct (%)68.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean28669.12
Minimum2
Maximum2886707
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:33.089742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile878.85
Q13835
median8751.5
Q321353.75
95-th percentile119595.5
Maximum2886707
Range2886705
Interquartile range (IQR)17518.75

Descriptive statistics

Standard deviation87895.777
Coefficient of variation (CV)3.0658694
Kurtosis359.39729
Mean28669.12
Median Absolute Deviation (MAD)6177.5
Skewness14.618338
Sum7.2068433 × 108
Variance7.7256677 × 109
MonotonicityNot monotonic
2025-12-08T12:04:33.162791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1768
 
< 0.1%
30318
 
< 0.1%
30137
 
< 0.1%
17447
 
< 0.1%
109517
 
< 0.1%
9447
 
< 0.1%
33057
 
< 0.1%
26337
 
< 0.1%
42487
 
< 0.1%
24387
 
< 0.1%
Other values (17243)25066
99.7%
ValueCountFrequency (%)
21
 
< 0.1%
92
< 0.1%
111
 
< 0.1%
121
 
< 0.1%
131
 
< 0.1%
143
< 0.1%
152
< 0.1%
171
 
< 0.1%
281
 
< 0.1%
311
 
< 0.1%
ValueCountFrequency (%)
28867071
< 0.1%
28847641
< 0.1%
28630161
< 0.1%
28433131
< 0.1%
27965851
< 0.1%
27405981
< 0.1%
26925541
< 0.1%
26672421
< 0.1%
14322661
< 0.1%
14272661
< 0.1%

HOUSEHOLDS_TOTAL
Real number (ℝ)

High correlation 

Distinct18143
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37193.526
Minimum27
Maximum3306109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:33.235148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile1135.8
Q14232.5
median9818
Q325745
95-th percentile157562.8
Maximum3306109
Range3306082
Interquartile range (IQR)21512.5

Descriptive statistics

Standard deviation112644.2
Coefficient of variation (CV)3.028597
Kurtosis266.41012
Mean37193.526
Median Absolute Deviation (MAD)6992
Skewness12.600778
Sum9.3500806 × 108
Variance1.2688717 × 1010
MonotonicityNot monotonic
2025-12-08T12:04:33.309953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24278
 
< 0.1%
16817
 
< 0.1%
32957
 
< 0.1%
33107
 
< 0.1%
33037
 
< 0.1%
42167
 
< 0.1%
40197
 
< 0.1%
19837
 
< 0.1%
40166
 
< 0.1%
82046
 
< 0.1%
Other values (18133)25070
99.7%
ValueCountFrequency (%)
271
 
< 0.1%
311
 
< 0.1%
333
< 0.1%
351
 
< 0.1%
371
 
< 0.1%
451
 
< 0.1%
463
< 0.1%
471
 
< 0.1%
481
 
< 0.1%
531
 
< 0.1%
ValueCountFrequency (%)
33061091
< 0.1%
32951981
< 0.1%
32818451
< 0.1%
32630691
< 0.1%
32423911
< 0.1%
32303831
< 0.1%
32185181
< 0.1%
32185111
< 0.1%
19630701
< 0.1%
19565611
< 0.1%

HOUSEHOLDS_SNAP
Real number (ℝ)

High correlation 

Distinct8157
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4566.3903
Minimum0
Maximum308063
Zeros32
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:33.483388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile82.9
Q1541
median1411
Q33415.5
95-th percentile16890.3
Maximum308063
Range308063
Interquartile range (IQR)2874.5

Descriptive statistics

Standard deviation14003.857
Coefficient of variation (CV)3.0667236
Kurtosis156.16657
Mean4566.3903
Median Absolute Deviation (MAD)1077
Skewness10.60147
Sum1.1479448 × 108
Variance1.9610801 × 108
MonotonicityNot monotonic
2025-12-08T12:04:33.557456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032
 
0.1%
8927
 
0.1%
7525
 
0.1%
1324
 
0.1%
8324
 
0.1%
2323
 
0.1%
823
 
0.1%
9423
 
0.1%
16722
 
0.1%
23322
 
0.1%
Other values (8147)24894
99.0%
ValueCountFrequency (%)
032
0.1%
12
 
< 0.1%
28
 
< 0.1%
317
0.1%
418
0.1%
520
0.1%
615
0.1%
718
0.1%
823
0.1%
916
0.1%
ValueCountFrequency (%)
3080631
< 0.1%
3048441
< 0.1%
3045141
< 0.1%
2968091
< 0.1%
2961931
< 0.1%
2943721
< 0.1%
2917731
< 0.1%
2908801
< 0.1%
2861521
< 0.1%
2755001
< 0.1%

POVERTY_RATE
Real number (ℝ)

High correlation 

Distinct25069
Distinct (%)99.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean16.292368
Minimum0
Maximum55.096502
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:33.628574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.352747
Q111.624456
median15.512302
Q319.84725
95-th percentile27.950051
Maximum55.096502
Range55.096502
Interquartile range (IQR)8.2227938

Descriptive statistics

Standard deviation6.5191004
Coefficient of variation (CV)0.40013215
Kurtosis1.6916002
Mean16.292368
Median Absolute Deviation (MAD)4.0738333
Skewness0.95381315
Sum409557.55
Variance42.49867
MonotonicityNot monotonic
2025-12-08T12:04:33.704048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.111111113
 
< 0.1%
8.1967213113
 
< 0.1%
103
 
< 0.1%
8.1521739133
 
< 0.1%
15.189873422
 
< 0.1%
11.538461542
 
< 0.1%
11.92660552
 
< 0.1%
12.941176472
 
< 0.1%
11.537943122
 
< 0.1%
8.6286594762
 
< 0.1%
Other values (25059)25114
99.9%
ValueCountFrequency (%)
02
< 0.1%
0.92449922961
< 0.1%
1.0401188711
< 0.1%
1.4245014251
< 0.1%
1.511879051
< 0.1%
1.8104366351
< 0.1%
2.0674646351
< 0.1%
2.3031050791
< 0.1%
2.4292603041
< 0.1%
2.5679758311
< 0.1%
ValueCountFrequency (%)
55.096501811
< 0.1%
53.949603621
< 0.1%
53.491525421
< 0.1%
53.324780841
< 0.1%
53.16167841
< 0.1%
52.627388541
< 0.1%
51.957877521
< 0.1%
51.887192541
< 0.1%
49.723596161
< 0.1%
49.453490971
< 0.1%

UNEMPLOYMENT_RATE
Real number (ℝ)

High correlation 

Distinct25059
Distinct (%)99.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean40.532816
Minimum3.2258065
Maximum88.407067
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:33.779366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.2258065
5-th percentile29.22547
Q134.880756
median39.85941
Q345.433666
95-th percentile54.321421
Maximum88.407067
Range85.18126
Interquartile range (IQR)10.55291

Descriptive statistics

Standard deviation7.8845164
Coefficient of variation (CV)0.1945218
Kurtosis0.77775158
Mean40.532816
Median Absolute Deviation (MAD)5.217136
Skewness0.52197197
Sum1018913.9
Variance62.165599
MonotonicityNot monotonic
2025-12-08T12:04:33.849934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
506
 
< 0.1%
33.333333334
 
< 0.1%
404
 
< 0.1%
37.54
 
< 0.1%
41.666666673
 
< 0.1%
40.909090913
 
< 0.1%
35.353535353
 
< 0.1%
37.714285712
 
< 0.1%
41.419310822
 
< 0.1%
33.238636362
 
< 0.1%
Other values (25049)25105
99.9%
ValueCountFrequency (%)
3.2258064521
< 0.1%
6.0752502591
< 0.1%
6.9204152251
< 0.1%
8.0193570691
< 0.1%
9.8886263921
< 0.1%
11.199664431
< 0.1%
11.392405061
< 0.1%
11.632653061
< 0.1%
13.103012431
< 0.1%
13.501534271
< 0.1%
ValueCountFrequency (%)
88.407066731
< 0.1%
86.371681421
< 0.1%
85.47113291
< 0.1%
81.535879141
< 0.1%
79.413003791
< 0.1%
78.329932961
< 0.1%
76.797869191
< 0.1%
76.564532981
< 0.1%
76.388102981
< 0.1%
76.286149161
< 0.1%

SNAP_RECEIPT_RATE
Real number (ℝ)

High correlation 

Distinct24877
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.513069
Minimum0
Maximum59.869976
Zeros32
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:33.918151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.476597
Q18.7876969
median12.730132
Q317.214771
95-th percentile25.416573
Maximum59.869976
Range59.869976
Interquartile range (IQR)8.4270744

Descriptive statistics

Standard deviation6.5687406
Coefficient of variation (CV)0.4861028
Kurtosis1.8539892
Mean13.513069
Median Absolute Deviation (MAD)4.1619678
Skewness0.96085378
Sum339705.04
Variance43.148353
MonotonicityNot monotonic
2025-12-08T12:04:33.990438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032
 
0.1%
14.285714298
 
< 0.1%
3.2258064525
 
< 0.1%
11.111111115
 
< 0.1%
12.55
 
< 0.1%
9.0909090915
 
< 0.1%
7.4074074075
 
< 0.1%
4.4444444445
 
< 0.1%
12.244897964
 
< 0.1%
8.3333333334
 
< 0.1%
Other values (24867)25061
99.7%
ValueCountFrequency (%)
032
0.1%
0.18916362651
 
< 0.1%
0.31876138431
 
< 0.1%
0.34129692831
 
< 0.1%
0.36297640651
 
< 0.1%
0.42105263161
 
< 0.1%
0.589101621
 
< 0.1%
0.60606060611
 
< 0.1%
0.61349693251
 
< 0.1%
0.62608232321
 
< 0.1%
ValueCountFrequency (%)
59.869976361
< 0.1%
57.990867581
< 0.1%
56.689734721
< 0.1%
54.897260271
< 0.1%
54.706684861
< 0.1%
54.070112891
< 0.1%
53.51063831
< 0.1%
52.781065091
< 0.1%
52.582159621
< 0.1%
52.236238531
< 0.1%

County
Text

Distinct1836
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:34.142861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length21
Mean length7.047655
Min length3

Characters and Unicode

Total characters177171
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowclay
2nd rowcumberland
3rd rowguilford
4th rowjackson
5th rowpasquotank
ValueCountFrequency (%)
washington248
 
0.9%
jefferson224
 
0.8%
st208
 
0.8%
franklin208
 
0.8%
jackson192
 
0.7%
lincoln192
 
0.7%
madison160
 
0.6%
clay144
 
0.5%
lake144
 
0.5%
union144
 
0.5%
Other values (1860)25135
93.1%
2025-12-08T12:04:34.348975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a18384
 
10.4%
e17770
 
10.0%
n15531
 
8.8%
o14243
 
8.0%
r13115
 
7.4%
l11896
 
6.7%
s10206
 
5.8%
i9540
 
5.4%
t8760
 
4.9%
c7267
 
4.1%
Other values (22)50459
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)177171
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a18384
 
10.4%
e17770
 
10.0%
n15531
 
8.8%
o14243
 
8.0%
r13115
 
7.4%
l11896
 
6.7%
s10206
 
5.8%
i9540
 
5.4%
t8760
 
4.9%
c7267
 
4.1%
Other values (22)50459
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)177171
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a18384
 
10.4%
e17770
 
10.0%
n15531
 
8.8%
o14243
 
8.0%
r13115
 
7.4%
l11896
 
6.7%
s10206
 
5.8%
i9540
 
5.4%
t8760
 
4.9%
c7267
 
4.1%
Other values (22)50459
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)177171
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a18384
 
10.4%
e17770
 
10.0%
n15531
 
8.8%
o14243
 
8.0%
r13115
 
7.4%
l11896
 
6.7%
s10206
 
5.8%
i9540
 
5.4%
t8760
 
4.9%
c7267
 
4.1%
Other values (22)50459
28.5%

State
Text

Distinct51
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:34.433891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters50278
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNC
2nd rowNC
3rd rowNC
4th rowNC
5th rowNC
ValueCountFrequency (%)
tx2032
 
8.1%
ga1272
 
5.1%
va1067
 
4.2%
ky960
 
3.8%
mo920
 
3.7%
ks840
 
3.3%
il816
 
3.2%
nc800
 
3.2%
ia792
 
3.2%
tn760
 
3.0%
Other values (41)14880
59.2%
2025-12-08T12:04:34.574641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A6555
13.0%
N5304
 
10.5%
M4080
 
8.1%
I4016
 
8.0%
T3648
 
7.3%
O3040
 
6.0%
K2648
 
5.3%
L2400
 
4.8%
S2392
 
4.8%
C2216
 
4.4%
Other values (14)13979
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)50278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A6555
13.0%
N5304
 
10.5%
M4080
 
8.1%
I4016
 
8.0%
T3648
 
7.3%
O3040
 
6.0%
K2648
 
5.3%
L2400
 
4.8%
S2392
 
4.8%
C2216
 
4.4%
Other values (14)13979
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)50278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A6555
13.0%
N5304
 
10.5%
M4080
 
8.1%
I4016
 
8.0%
T3648
 
7.3%
O3040
 
6.0%
K2648
 
5.3%
L2400
 
4.8%
S2392
 
4.8%
C2216
 
4.4%
Other values (14)13979
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)50278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A6555
13.0%
N5304
 
10.5%
M4080
 
8.1%
I4016
 
8.0%
T3648
 
7.3%
O3040
 
6.0%
K2648
 
5.3%
L2400
 
4.8%
S2392
 
4.8%
C2216
 
4.4%
Other values (14)13979
27.8%

Food_Insecurity_Rate
Real number (ℝ)

High correlation 

Distinct317
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14183973
Minimum0.024
Maximum0.379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:34.634212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.024
5-th percentile0.083
Q10.115
median0.138
Q30.164
95-th percentile0.216
Maximum0.379
Range0.355
Interquartile range (IQR)0.049

Descriptive statistics

Standard deviation0.041016287
Coefficient of variation (CV)0.28917347
Kurtosis1.72694
Mean0.14183973
Median Absolute Deviation (MAD)0.024
Skewness0.80863339
Sum3565.709
Variance0.0016823358
MonotonicityNot monotonic
2025-12-08T12:04:34.703030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.134312
 
1.2%
0.142302
 
1.2%
0.125298
 
1.2%
0.135288
 
1.1%
0.131285
 
1.1%
0.138284
 
1.1%
0.13284
 
1.1%
0.133283
 
1.1%
0.141282
 
1.1%
0.117280
 
1.1%
Other values (307)22241
88.5%
ValueCountFrequency (%)
0.0241
 
< 0.1%
0.0291
 
< 0.1%
0.0331
 
< 0.1%
0.0342
< 0.1%
0.0363
< 0.1%
0.0374
< 0.1%
0.0381
 
< 0.1%
0.0392
< 0.1%
0.042
< 0.1%
0.0414
< 0.1%
ValueCountFrequency (%)
0.3791
< 0.1%
0.3751
< 0.1%
0.3631
< 0.1%
0.3611
< 0.1%
0.3581
< 0.1%
0.3571
< 0.1%
0.3521
< 0.1%
0.3482
< 0.1%
0.3461
< 0.1%
0.3442
< 0.1%

Num_Food_Insecure_Persons
Real number (ℝ)

High correlation 

Distinct4578
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13747.877
Minimum10
Maximum1749600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:34.771616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile320
Q11560
median3760
Q39450
95-th percentile55280
Maximum1749600
Range1749590
Interquartile range (IQR)7890

Descriptive statistics

Standard deviation45535.84
Coefficient of variation (CV)3.3122087
Kurtosis343.67167
Mean13747.877
Median Absolute Deviation (MAD)2740
Skewness14.417779
Sum3.4560789 × 108
Variance2.0735127 × 109
MonotonicityNot monotonic
2025-12-08T12:04:34.843180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9058
 
0.2%
112055
 
0.2%
12055
 
0.2%
30055
 
0.2%
63055
 
0.2%
127055
 
0.2%
29054
 
0.2%
52053
 
0.2%
57053
 
0.2%
66053
 
0.2%
Other values (4568)24593
97.8%
ValueCountFrequency (%)
107
 
< 0.1%
2010
 
< 0.1%
304
 
< 0.1%
4016
 
0.1%
5015
 
0.1%
6022
 
0.1%
7022
 
0.1%
8046
0.2%
9058
0.2%
10052
0.2%
ValueCountFrequency (%)
17496001
< 0.1%
16039101
< 0.1%
14521301
< 0.1%
13931701
< 0.1%
12249401
< 0.1%
11470101
< 0.1%
11462901
< 0.1%
11357101
< 0.1%
8606701
< 0.1%
7970901
< 0.1%

Low_Threshold_State
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.5 KiB
1.3
14017 
2.0
5292 
1.65
3502 
1.6
 
1328
1.85
 
1000

Length

Max length4
Median length3
Mean length3.1790843
Min length3

Characters and Unicode

Total characters79919
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.314017
55.8%
2.05292
 
21.1%
1.653502
 
13.9%
1.61328
 
5.3%
1.851000
 
4.0%

Length

2025-12-08T12:04:34.906353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-08T12:04:34.946976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.314017
55.8%
2.05292
 
21.1%
1.653502
 
13.9%
1.61328
 
5.3%
1.851000
 
4.0%

Most occurring characters

ValueCountFrequency (%)
.25139
31.5%
119847
24.8%
314017
17.5%
25292
 
6.6%
05292
 
6.6%
64830
 
6.0%
54502
 
5.6%
81000
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)79919
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.25139
31.5%
119847
24.8%
314017
17.5%
25292
 
6.6%
05292
 
6.6%
64830
 
6.0%
54502
 
5.6%
81000
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)79919
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.25139
31.5%
119847
24.8%
314017
17.5%
25292
 
6.6%
05292
 
6.6%
64830
 
6.0%
54502
 
5.6%
81000
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)79919
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.25139
31.5%
119847
24.8%
314017
17.5%
25292
 
6.6%
05292
 
6.6%
64830
 
6.0%
54502
 
5.6%
81000
 
1.3%

Low_Threshold_Type
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.5 KiB
SNAP
25139 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters100556
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSNAP
2nd rowSNAP
3rd rowSNAP
4th rowSNAP
5th rowSNAP

Common Values

ValueCountFrequency (%)
SNAP25139
100.0%

Length

2025-12-08T12:04:35.105498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-08T12:04:35.138995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
snap25139
100.0%

Most occurring characters

ValueCountFrequency (%)
S25139
25.0%
N25139
25.0%
A25139
25.0%
P25139
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)100556
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S25139
25.0%
N25139
25.0%
A25139
25.0%
P25139
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100556
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S25139
25.0%
N25139
25.0%
A25139
25.0%
P25139
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100556
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S25139
25.0%
N25139
25.0%
A25139
25.0%
P25139
25.0%

High_Threshold_State
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.5 KiB
1.85
19847 
2.0
5292 

Length

Max length4
Median length4
Mean length3.7894904
Min length3

Characters and Unicode

Total characters95264
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.8519847
78.9%
2.05292
 
21.1%

Length

2025-12-08T12:04:35.175672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-08T12:04:35.207668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.8519847
78.9%
2.05292
 
21.1%

Most occurring characters

ValueCountFrequency (%)
.25139
26.4%
119847
20.8%
819847
20.8%
519847
20.8%
25292
 
5.6%
05292
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)95264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.25139
26.4%
119847
20.8%
819847
20.8%
519847
20.8%
25292
 
5.6%
05292
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)95264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.25139
26.4%
119847
20.8%
819847
20.8%
519847
20.8%
25292
 
5.6%
05292
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)95264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.25139
26.4%
119847
20.8%
819847
20.8%
519847
20.8%
25292
 
5.6%
05292
 
5.6%

High_Threshold_Type
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.5 KiB
Other Nutrition Program
18847 
SNAP
6292 

Length

Max length23
Median length23
Mean length18.24452
Min length4

Characters and Unicode

Total characters458649
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSNAP
2nd rowSNAP
3rd rowSNAP
4th rowSNAP
5th rowSNAP

Common Values

ValueCountFrequency (%)
Other Nutrition Program18847
75.0%
SNAP6292
 
25.0%

Length

2025-12-08T12:04:35.255394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-08T12:04:35.293397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
other18847
30.0%
nutrition18847
30.0%
program18847
30.0%
snap6292
 
10.0%

Most occurring characters

ValueCountFrequency (%)
r75388
16.4%
t56541
12.3%
o37694
 
8.2%
i37694
 
8.2%
37694
 
8.2%
P25139
 
5.5%
N25139
 
5.5%
O18847
 
4.1%
h18847
 
4.1%
u18847
 
4.1%
Other values (7)106819
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)458649
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r75388
16.4%
t56541
12.3%
o37694
 
8.2%
i37694
 
8.2%
37694
 
8.2%
P25139
 
5.5%
N25139
 
5.5%
O18847
 
4.1%
h18847
 
4.1%
u18847
 
4.1%
Other values (7)106819
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)458649
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r75388
16.4%
t56541
12.3%
o37694
 
8.2%
i37694
 
8.2%
37694
 
8.2%
P25139
 
5.5%
N25139
 
5.5%
O18847
 
4.1%
h18847
 
4.1%
u18847
 
4.1%
Other values (7)106819
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)458649
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r75388
16.4%
t56541
12.3%
o37694
 
8.2%
i37694
 
8.2%
37694
 
8.2%
P25139
 
5.5%
N25139
 
5.5%
O18847
 
4.1%
h18847
 
4.1%
u18847
 
4.1%
Other values (7)106819
23.3%

Pct_FI_Below_Low_Threshold
Real number (ℝ)

High correlation 

Distinct785
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61143446
Minimum0.131
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:35.344346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.131
5-th percentile0.389
Q10.52
median0.609
Q30.699
95-th percentile0.845
Maximum1
Range0.869
Interquartile range (IQR)0.179

Descriptive statistics

Standard deviation0.13759261
Coefficient of variation (CV)0.22503247
Kurtosis0.09619063
Mean0.61143446
Median Absolute Deviation (MAD)0.089
Skewness0.15628991
Sum15370.851
Variance0.018931725
MonotonicityNot monotonic
2025-12-08T12:04:35.416719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1219
 
0.9%
0.61793
 
0.4%
0.57792
 
0.4%
0.61892
 
0.4%
0.58989
 
0.4%
0.61689
 
0.4%
0.57588
 
0.4%
0.63587
 
0.3%
0.58887
 
0.3%
0.61187
 
0.3%
Other values (775)24116
95.9%
ValueCountFrequency (%)
0.1311
< 0.1%
0.1471
< 0.1%
0.1511
< 0.1%
0.1631
< 0.1%
0.1651
< 0.1%
0.1671
< 0.1%
0.1831
< 0.1%
0.1851
< 0.1%
0.1871
< 0.1%
0.1891
< 0.1%
ValueCountFrequency (%)
1219
0.9%
0.9991
 
< 0.1%
0.9981
 
< 0.1%
0.9972
 
< 0.1%
0.9964
 
< 0.1%
0.9942
 
< 0.1%
0.9922
 
< 0.1%
0.9913
 
< 0.1%
0.991
 
< 0.1%
0.9891
 
< 0.1%

Pct_FI_Between_Thresholds
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct434
Distinct (%)2.1%
Missing4133
Missing (%)16.4%
Infinite0
Infinite (%)0.0%
Mean0.13262249
Minimum0
Maximum0.773
Zeros2273
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:35.487420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.068
median0.138
Q30.189
95-th percentile0.267
Maximum0.773
Range0.773
Interquartile range (IQR)0.121

Descriptive statistics

Standard deviation0.083496027
Coefficient of variation (CV)0.62957668
Kurtosis0.29881038
Mean0.13262249
Median Absolute Deviation (MAD)0.059
Skewness0.29646651
Sum2785.868
Variance0.0069715865
MonotonicityNot monotonic
2025-12-08T12:04:35.561918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02273
 
9.0%
0.168133
 
0.5%
0.171130
 
0.5%
0.17121
 
0.5%
0.165120
 
0.5%
0.162120
 
0.5%
0.152119
 
0.5%
0.159117
 
0.5%
0.173113
 
0.4%
0.144113
 
0.4%
Other values (424)17647
70.2%
(Missing)4133
 
16.4%
ValueCountFrequency (%)
02273
9.0%
0.0016
 
< 0.1%
0.0029
 
< 0.1%
0.0039
 
< 0.1%
0.00411
 
< 0.1%
0.0059
 
< 0.1%
0.00612
 
< 0.1%
0.0078
 
< 0.1%
0.00812
 
< 0.1%
0.00918
 
0.1%
ValueCountFrequency (%)
0.7731
< 0.1%
0.7371
< 0.1%
0.6711
< 0.1%
0.6561
< 0.1%
0.591
< 0.1%
0.5831
< 0.1%
0.5471
< 0.1%
0.531
< 0.1%
0.5191
< 0.1%
0.5082
< 0.1%

Pct_FI_Above_High_Threshold
Real number (ℝ)

High correlation  Zeros 

Distinct680
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27774721
Minimum0
Maximum0.81
Zeros563
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:35.635965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.073
Q10.198
median0.277
Q30.355
95-th percentile0.482
Maximum0.81
Range0.81
Interquartile range (IQR)0.157

Descriptive statistics

Standard deviation0.12164156
Coefficient of variation (CV)0.43795783
Kurtosis0.15372667
Mean0.27774721
Median Absolute Deviation (MAD)0.079
Skewness0.10823439
Sum6982.287
Variance0.01479667
MonotonicityNot monotonic
2025-12-08T12:04:35.712548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0563
 
2.2%
0.306109
 
0.4%
0.268104
 
0.4%
0.255101
 
0.4%
0.275100
 
0.4%
0.284100
 
0.4%
0.27399
 
0.4%
0.26999
 
0.4%
0.2598
 
0.4%
0.27798
 
0.4%
Other values (670)23668
94.1%
ValueCountFrequency (%)
0563
2.2%
0.0013
 
< 0.1%
0.0024
 
< 0.1%
0.0034
 
< 0.1%
0.0042
 
< 0.1%
0.0057
 
< 0.1%
0.0066
 
< 0.1%
0.0071
 
< 0.1%
0.0083
 
< 0.1%
0.0094
 
< 0.1%
ValueCountFrequency (%)
0.811
 
< 0.1%
0.7991
 
< 0.1%
0.7951
 
< 0.1%
0.7781
 
< 0.1%
0.7751
 
< 0.1%
0.7621
 
< 0.1%
0.7611
 
< 0.1%
0.7361
 
< 0.1%
0.7311
 
< 0.1%
0.7273
< 0.1%
Distinct359
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:35.880005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.8890568
Min length2

Characters and Unicode

Total characters122906
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)0.1%

Sample

1st row0.312
2nd row0.237
3rd row0.231
4th row0.261
5th row0.25
ValueCountFrequency (%)
0.198207
 
0.8%
0.187205
 
0.8%
0.203205
 
0.8%
0.199204
 
0.8%
0.209202
 
0.8%
0.23201
 
0.8%
0.201201
 
0.8%
0.216200
 
0.8%
0.218198
 
0.8%
0.215197
 
0.8%
Other values (349)23119
92.0%
2025-12-08T12:04:36.090766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
027784
22.6%
.25137
20.5%
218587
15.1%
114713
12.0%
36271
 
5.1%
85235
 
4.3%
65108
 
4.2%
95104
 
4.2%
75087
 
4.1%
54980
 
4.1%
Other values (3)4900
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)122906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
027784
22.6%
.25137
20.5%
218587
15.1%
114713
12.0%
36271
 
5.1%
85235
 
4.3%
65108
 
4.2%
95104
 
4.2%
75087
 
4.1%
54980
 
4.1%
Other values (3)4900
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)122906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
027784
22.6%
.25137
20.5%
218587
15.1%
114713
12.0%
36271
 
5.1%
85235
 
4.3%
65108
 
4.2%
95104
 
4.2%
75087
 
4.1%
54980
 
4.1%
Other values (3)4900
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)122906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
027784
22.6%
.25137
20.5%
218587
15.1%
114713
12.0%
36271
 
5.1%
85235
 
4.3%
65108
 
4.2%
95104
 
4.2%
75087
 
4.1%
54980
 
4.1%
Other values (3)4900
 
4.0%

Num_Food_Insecure_Children
Real number (ℝ)

High correlation 

Distinct2671
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4710.6858
Minimum0
Maximum650480
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:36.155677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile110
Q1540
median1310
Q33260
95-th percentile18670
Maximum650480
Range650480
Interquartile range (IQR)2720

Descriptive statistics

Standard deviation16261.486
Coefficient of variation (CV)3.4520421
Kurtosis431.9012
Mean4710.6858
Median Absolute Deviation (MAD)960
Skewness16.25592
Sum1.1842193 × 108
Variance2.6443592 × 108
MonotonicityNot monotonic
2025-12-08T12:04:36.225347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
280160
 
0.6%
50154
 
0.6%
400150
 
0.6%
40144
 
0.6%
100142
 
0.6%
310141
 
0.6%
80138
 
0.5%
200137
 
0.5%
210137
 
0.5%
270136
 
0.5%
Other values (2661)23700
94.3%
ValueCountFrequency (%)
015
 
0.1%
1040
 
0.2%
2065
0.3%
30114
0.5%
40144
0.6%
50154
0.6%
60112
0.4%
70114
0.5%
80138
0.5%
90122
0.5%
ValueCountFrequency (%)
6504801
< 0.1%
6200901
< 0.1%
5909101
< 0.1%
5361001
< 0.1%
4815401
< 0.1%
4390101
< 0.1%
4139101
< 0.1%
3422501
< 0.1%
3054801
< 0.1%
2988601
< 0.1%
Distinct623
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:36.393019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9918851
Min length1

Characters and Unicode

Total characters100352
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)0.5%

Sample

1st row0.841
2nd row0.7
3rd row0.669
4th row0.72
5th row0.658
ValueCountFrequency (%)
0.73820
 
3.3%
0.76777
 
3.1%
0.77756
 
3.0%
0.71739
 
2.9%
0.72739
 
2.9%
0.74734
 
2.9%
0.7727
 
2.9%
0.75724
 
2.9%
0.8722
 
2.9%
0.79709
 
2.8%
Other values (613)17692
70.4%
2025-12-08T12:04:36.611387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
025266
25.2%
.24966
24.9%
711248
11.2%
68500
 
8.5%
88085
 
8.1%
55258
 
5.2%
94301
 
4.3%
43703
 
3.7%
13154
 
3.1%
33079
 
3.1%
Other values (3)2792
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)100352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
025266
25.2%
.24966
24.9%
711248
11.2%
68500
 
8.5%
88085
 
8.1%
55258
 
5.2%
94301
 
4.3%
43703
 
3.7%
13154
 
3.1%
33079
 
3.1%
Other values (3)2792
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
025266
25.2%
.24966
24.9%
711248
11.2%
68500
 
8.5%
88085
 
8.1%
55258
 
5.2%
94301
 
4.3%
43703
 
3.7%
13154
 
3.1%
33079
 
3.1%
Other values (3)2792
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
025266
25.2%
.24966
24.9%
711248
11.2%
68500
 
8.5%
88085
 
8.1%
55258
 
5.2%
94301
 
4.3%
43703
 
3.7%
13154
 
3.1%
33079
 
3.1%
Other values (3)2792
 
2.8%
Distinct623
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:36.770894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9929591
Min length1

Characters and Unicode

Total characters100379
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)0.5%

Sample

1st row0.159
2nd row0.3
3rd row0.331
4th row0.28
5th row0.342
ValueCountFrequency (%)
0.27797
 
3.2%
0.24777
 
3.1%
0.28755
 
3.0%
0.29752
 
3.0%
0.23743
 
3.0%
0.26731
 
2.9%
0.25730
 
2.9%
0.3718
 
2.9%
0.22718
 
2.9%
0.2711
 
2.8%
Other values (613)17707
70.4%
2025-12-08T12:04:36.987033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
026912
26.8%
.24969
24.9%
211183
11.1%
39028
 
9.0%
17582
 
7.6%
45472
 
5.5%
53810
 
3.8%
62977
 
3.0%
72922
 
2.9%
92772
 
2.8%
Other values (3)2752
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)100379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
026912
26.8%
.24969
24.9%
211183
11.1%
39028
 
9.0%
17582
 
7.6%
45472
 
5.5%
53810
 
3.8%
62977
 
3.0%
72922
 
2.9%
92772
 
2.8%
Other values (3)2752
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
026912
26.8%
.24969
24.9%
211183
11.1%
39028
 
9.0%
17582
 
7.6%
45472
 
5.5%
53810
 
3.8%
62977
 
3.0%
72922
 
2.9%
92772
 
2.8%
Other values (3)2752
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
026912
26.8%
.24969
24.9%
211183
11.1%
39028
 
9.0%
17582
 
7.6%
45472
 
5.5%
53810
 
3.8%
62977
 
3.0%
72922
 
2.9%
92772
 
2.8%
Other values (3)2752
 
2.7%

Cost_Per_Meal
Real number (ℝ)

High correlation 

Distinct3415
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.88748
Minimum1.85
Maximum6.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:37.052608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.85
5-th percentile2.44
Q12.68
median2.86
Q33.05
95-th percentile3.44
Maximum6.96
Range5.11
Interquartile range (IQR)0.37

Descriptive statistics

Standard deviation0.32459293
Coefficient of variation (CV)0.11241391
Kurtosis8.032325
Mean2.88748
Median Absolute Deviation (MAD)0.18
Skewness1.4810124
Sum72588.36
Variance0.10536057
MonotonicityNot monotonic
2025-12-08T12:04:37.225416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.81359
 
1.4%
2.86349
 
1.4%
2.93348
 
1.4%
2.96346
 
1.4%
2.92336
 
1.3%
2.91333
 
1.3%
2.89332
 
1.3%
2.88330
 
1.3%
2.82329
 
1.3%
2.75329
 
1.3%
Other values (3405)21748
86.5%
ValueCountFrequency (%)
1.851
 
< 0.1%
1.91
 
< 0.1%
1.9284735481
 
< 0.1%
1.963
< 0.1%
1.971
 
< 0.1%
1.982
< 0.1%
1.9817728411
 
< 0.1%
22
< 0.1%
2.014
< 0.1%
2.024
< 0.1%
ValueCountFrequency (%)
6.961
< 0.1%
6.451
< 0.1%
6.22
< 0.1%
6.191
< 0.1%
6.181
< 0.1%
6.091
< 0.1%
61
< 0.1%
5.851
< 0.1%
5.721
< 0.1%
5.71
< 0.1%

Annual_Food_Budget_Shortfall
Real number (ℝ)

High correlation 

Distinct12051
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7072572.7
Minimum4480
Maximum8.15943 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2025-12-08T12:04:37.293020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4480
5-th percentile162117
Q1762000
median1851000
Q34702000
95-th percentile28786100
Maximum8.15943 × 108
Range8.1593852 × 108
Interquartile range (IQR)3940000

Descriptive statistics

Standard deviation23889299
Coefficient of variation (CV)3.3777382
Kurtosis340.63176
Mean7072572.7
Median Absolute Deviation (MAD)1355000
Skewness14.354372
Sum1.7779741 × 1011
Variance5.7069862 × 1014
MonotonicityNot monotonic
2025-12-08T12:04:37.365602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8900019
 
0.1%
52900016
 
0.1%
8400015
 
0.1%
7800015
 
0.1%
20700015
 
0.1%
52100015
 
0.1%
71100014
 
0.1%
43900014
 
0.1%
112400014
 
0.1%
54200014
 
0.1%
Other values (12041)24988
99.4%
ValueCountFrequency (%)
44801
 
< 0.1%
45801
 
< 0.1%
50004
< 0.1%
60001
 
< 0.1%
91301
 
< 0.1%
100002
< 0.1%
110004
< 0.1%
120003
< 0.1%
140001
 
< 0.1%
150001
 
< 0.1%
ValueCountFrequency (%)
8159430001
< 0.1%
8157659601
< 0.1%
7903820001
< 0.1%
7741240001
< 0.1%
7214640001
< 0.1%
6721710001
< 0.1%
6569320001
< 0.1%
6498790001
< 0.1%
3741320001
< 0.1%
3656140001
< 0.1%

Interactions

2025-12-08T12:04:29.926249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:02.769196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:04.174255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:05.435439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:06.692649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:07.847630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:09.173353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:10.486595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:11.866392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:13.117019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:14.428010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:15.753071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:16.987800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:18.275469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:19.443480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:20.764453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:22.091201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:23.454144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:24.607792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:26.035722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:27.416731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:28.659540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:29.987950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:02.846775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:04.230961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:05.490055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:06.745477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:07.904379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:09.227602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-08T12:04:11.922867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:13.171851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:14.484124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:15.809156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:17.042330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:18.329183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:19.599131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:20.823400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:22.149558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-08T12:04:30.045622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:02.904521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:04.368383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:05.540997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-08T12:04:08.052217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:09.279606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:10.607349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:11.978070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-08T12:04:06.842579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:08.104067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:09.330451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:10.668424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:12.031334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:13.278982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:14.589733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:15.917918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:17.145102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-08T12:04:20.930432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-08T12:04:23.609381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-08T12:04:27.582003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:28.814037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:30.157940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:03.017906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:04.467794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:05.640168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:06.889799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:08.156366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-08T12:04:14.641373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-08T12:04:17.196671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:18.480953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:19.759748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:20.983400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:22.312661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-08T12:04:24.835782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-08T12:04:26.261356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-08T12:04:29.867948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-08T12:04:37.438545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Annual_Food_Budget_ShortfallCOUNTY_FIPSCost_Per_MealFIPSFood_Insecurity_RateHOUSEHOLDS_SNAPHOUSEHOLDS_TOTALHigh_Threshold_StateHigh_Threshold_TypeLow_Threshold_StateMEDIAN_HOUSEHOLD_INCOMENum_Food_Insecure_ChildrenNum_Food_Insecure_PersonsPOP_16_PLUSPOP_BELOW_POVERTYPOP_POVERTY_DETERMINEDPOP_UNEMPLOYEDPOVERTY_RATEPct_FI_Above_High_ThresholdPct_FI_Below_Low_ThresholdPct_FI_Between_ThresholdsSNAP_RECEIPT_RATESTATE_FIPSUNEMPLOYMENT_RATEYEAR
Annual_Food_Budget_Shortfall1.000-0.1000.117-0.0410.1760.9630.9700.0990.0960.0620.2030.9790.9960.9760.9790.9740.9820.0850.1450.020-0.1590.166-0.041-0.1450.004
COUNTY_FIPS-0.1001.000-0.1850.0610.103-0.089-0.1090.1970.2560.264-0.083-0.080-0.088-0.104-0.086-0.104-0.1000.088-0.067-0.0290.0100.0570.0360.050-0.000
Cost_Per_Meal0.117-0.1851.000-0.029-0.2100.0330.0840.1820.2720.1680.3270.0130.0470.0850.0380.0820.078-0.1930.191-0.0490.014-0.174-0.028-0.1080.501
FIPS-0.0410.061-0.0291.000-0.112-0.029-0.0110.4960.4500.4380.067-0.038-0.040-0.011-0.040-0.013-0.013-0.0850.0490.059-0.168-0.0440.999-0.018-0.000
Food_Insecurity_Rate0.1760.103-0.210-0.1121.0000.196-0.0100.1730.1750.159-0.6950.1380.1880.0040.209-0.0020.0810.795-0.3920.2400.0880.668-0.1130.566-0.161
HOUSEHOLDS_SNAP0.963-0.0890.033-0.0290.1961.0000.9420.1200.1370.0780.0960.9570.9670.9430.9790.9420.9620.1760.0240.137-0.1650.326-0.029-0.0660.022
HOUSEHOLDS_TOTAL0.970-0.1090.084-0.011-0.0100.9421.0000.1230.1220.0730.3270.9670.9710.9980.9510.9980.984-0.0690.217-0.022-0.1880.040-0.011-0.2630.001
High_Threshold_State0.0990.1970.1820.4960.1730.1200.1231.0000.8941.0000.1350.0870.0930.1070.0950.1110.1180.0950.0600.4480.4850.0690.5620.0270.057
High_Threshold_Type0.0960.2560.2720.4500.1750.1370.1220.8941.0001.0000.1770.0760.0810.0980.0910.1000.1100.1170.0440.4730.5390.0690.5040.0450.053
Low_Threshold_State0.0620.2640.1680.4380.1590.0780.0731.0001.0001.0000.1420.0580.0580.0620.0620.0640.0650.1230.1120.2780.4120.1000.4580.0850.038
MEDIAN_HOUSEHOLD_INCOME0.203-0.0830.3270.067-0.6950.0960.3270.1350.1770.1421.0000.2110.1840.3230.1070.3290.238-0.8160.687-0.450-0.164-0.6980.068-0.7190.169
Num_Food_Insecure_Children0.979-0.0800.013-0.0380.1380.9570.9670.0870.0760.0580.2111.0000.9850.9710.9760.9760.9690.0720.1140.038-0.1750.158-0.038-0.189-0.063
Num_Food_Insecure_Persons0.996-0.0880.047-0.0400.1880.9670.9710.0930.0810.0580.1840.9851.0000.9760.9840.9750.9830.0960.1350.021-0.1590.175-0.040-0.140-0.030
POP_16_PLUS0.976-0.1040.085-0.0110.0040.9430.9980.1070.0980.0620.3230.9710.9761.0000.9560.9980.989-0.0550.218-0.023-0.1900.051-0.011-0.2460.003
POP_BELOW_POVERTY0.979-0.0860.038-0.0400.2090.9790.9510.0950.0910.0620.1070.9760.9840.9561.0000.9560.9700.1990.0320.123-0.1740.252-0.041-0.091-0.004
POP_POVERTY_DETERMINED0.974-0.1040.082-0.013-0.0020.9420.9980.1110.1000.0640.3290.9760.9750.9980.9561.0000.984-0.0590.206-0.019-0.1830.048-0.013-0.2640.000
POP_UNEMPLOYED0.982-0.1000.078-0.0130.0810.9620.9840.1180.1100.0650.2380.9690.9830.9890.9700.9841.0000.0270.1680.021-0.1870.129-0.013-0.1230.018
POVERTY_RATE0.0850.088-0.193-0.0850.7950.176-0.0690.0950.1170.123-0.8160.0720.096-0.0550.199-0.0590.0271.000-0.6440.5260.0180.801-0.0860.641-0.022
Pct_FI_Above_High_Threshold0.145-0.0670.1910.049-0.3920.0240.2170.0600.0440.1120.6870.1140.1350.2180.0320.2060.168-0.6441.000-0.720-0.255-0.5900.050-0.4550.026
Pct_FI_Below_Low_Threshold0.020-0.029-0.0490.0590.2400.137-0.0220.4480.4730.278-0.4500.0380.021-0.0230.123-0.0190.0210.526-0.7201.000-0.3980.5030.0580.3660.072
Pct_FI_Between_Thresholds-0.1590.0100.014-0.1680.088-0.165-0.1880.4850.5390.412-0.164-0.175-0.159-0.190-0.174-0.183-0.1870.018-0.255-0.3981.0000.038-0.1680.0640.012
SNAP_RECEIPT_RATE0.1660.057-0.174-0.0440.6680.3260.0400.0690.0690.100-0.6980.1580.1750.0510.2520.0480.1290.801-0.5900.5030.0381.000-0.0450.6090.062
STATE_FIPS-0.0410.036-0.0280.999-0.113-0.029-0.0110.5620.5040.4580.068-0.038-0.040-0.011-0.041-0.013-0.013-0.0860.0500.058-0.168-0.0451.000-0.018-0.000
UNEMPLOYMENT_RATE-0.1450.050-0.108-0.0180.566-0.066-0.2630.0270.0450.085-0.719-0.189-0.140-0.246-0.091-0.264-0.1230.641-0.4550.3660.0640.609-0.0181.0000.098
YEAR0.004-0.0000.501-0.000-0.1610.0220.0010.0570.0530.0380.169-0.063-0.0300.003-0.0040.0000.018-0.0220.0260.0720.0120.062-0.0000.0981.000

Missing values

2025-12-08T12:04:31.385784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-08T12:04:31.560007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-08T12:04:31.736774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

YEARFIPSSTATE_FIPSCOUNTY_FIPSMEDIAN_HOUSEHOLD_INCOMEPOP_POVERTY_DETERMINEDPOP_BELOW_POVERTYPOP_16_PLUSPOP_UNEMPLOYEDHOUSEHOLDS_TOTALHOUSEHOLDS_SNAPPOVERTY_RATEUNEMPLOYMENT_RATESNAP_RECEIPT_RATECountyStateFood_Insecurity_RateNum_Food_Insecure_PersonsLow_Threshold_StateLow_Threshold_TypeHigh_Threshold_StateHigh_Threshold_TypePct_FI_Below_Low_ThresholdPct_FI_Between_ThresholdsPct_FI_Above_High_ThresholdChild_Food_Insecurity_RateNum_Food_Insecure_ChildrenPct_FI_Children_Below_185FPLPct_FI_Children_Above_185FPLCost_Per_MealAnnual_Food_Budget_Shortfall
0201137043374336711.010380.02262.08680.04258.0446457121.79190849.05530012.791219clayNC0.1531610.02.0SNAP2.0SNAP0.6830.00.3170.312620.00.8410.1592.74719180.0
1201137051375144861.0302057.050175.0240096.078349.01181171581916.61110332.63236413.392653cumberlandNC0.20364310.02.0SNAP2.0SNAP0.7180.00.2820.23720110.00.70.32.5226420430.0
2201137081378146288.0469463.076141.0382682.0125748.01920642091316.21874432.85965910.888558guilfordNC0.19895480.02.0SNAP2.0SNAP0.6710.00.3290.23126250.00.6690.3312.6441093880.0
3201137099379936826.035995.07028.033235.013872.015759162119.52493441.73913010.286186jacksonNC0.1616380.02.0SNAP2.0SNAP0.6700.00.3300.2611830.00.720.282.822933120.0
42011371393713945298.038228.07589.032283.011352.014550230119.85194135.16401815.814433pasquotankNC0.2108510.02.0SNAP2.0SNAP0.6630.00.3370.252350.00.6580.3422.663690390.0
52011371553715530874.0129366.039625.0100916.046758.0445281111430.63015046.33358424.959576robesonNC0.23531200.02.0SNAP2.0SNAP0.8890.00.1110.35212710.00.8420.1582.3211800580.0
62011371733717340719.013682.03072.011117.05138.0545544522.45285846.2175058.157654swainNC0.1832550.02.0SNAP2.0SNAP0.6270.00.3730.3291050.00.730.272.721130760.0
72011371833718365289.0858079.086939.0674186.0188518.03343021883410.13181827.9623135.633828wakeNC0.150132320.02.0SNAP2.0SNAP0.5070.00.4930.19344220.00.5330.4672.7659538150.0
820113700737734659.024333.05256.021488.09635.09688170721.60029644.83898017.619736ansonNC0.2326210.02.0SNAP2.0SNAP0.7810.00.2190.2761660.00.7230.2772.562591750.0
92011371113711135230.043761.08097.036167.014736.017483232618.50277640.74432513.304353mcdowellNC0.1767890.02.0SNAP2.0SNAP0.6870.00.3130.323130.00.740.262.703472970.0
YEARFIPSSTATE_FIPSCOUNTY_FIPSMEDIAN_HOUSEHOLD_INCOMEPOP_POVERTY_DETERMINEDPOP_BELOW_POVERTYPOP_16_PLUSPOP_UNEMPLOYEDHOUSEHOLDS_TOTALHOUSEHOLDS_SNAPPOVERTY_RATEUNEMPLOYMENT_RATESNAP_RECEIPT_RATECountyStateFood_Insecurity_RateNum_Food_Insecure_PersonsLow_Threshold_StateLow_Threshold_TypeHigh_Threshold_StateHigh_Threshold_TypePct_FI_Below_Low_ThresholdPct_FI_Between_ThresholdsPct_FI_Above_High_ThresholdChild_Food_Insecurity_RateNum_Food_Insecure_ChildrenPct_FI_Children_Below_185FPLPct_FI_Children_Above_185FPLCost_Per_MealAnnual_Food_Budget_Shortfall
25129201813063136345778.0272704.054635.0208902.070267.0928452087520.03454333.63634622.483709claytonGA0.14941640.01.3SNAP1.85Other Nutrition Program0.6780.1190.2030.19114950.00.520.482.7419309000.0
251302018131391313959898.0193957.028319.0151110.055549.063931670614.60065936.76063810.489434hallGA0.10019570.01.3SNAP1.85Other Nutrition Program0.4330.3160.2510.136620.0103.0710163000.0
2513120181500915977117.0162740.015840.0132234.043955.05427453799.73331733.2403169.910823mauiHI0.10417240.02.0SNAP2.00SNAP0.557NaN0.4430.1696160.00.50.53.8711288000.0
25132201816071167151058.04295.0559.03219.01428.0158512313.01513444.3616037.760252oneidaID0.108470.01.3SNAP1.85Other Nutrition Program0.5300.2450.2250.139180.00.820.183.06243000.0
25133201816045164543001.016898.03091.013580.06613.0658387918.29210648.69661313.352575gemID0.1392380.01.3SNAP1.85Other Nutrition Program0.5680.1710.2610.181720.00.790.213.301329000.0
2513420181600316345319.03991.0453.03387.01680.0167511411.35053949.6014176.805970adamsID0.133540.01.3SNAP1.85Other Nutrition Program0.5030.2930.2040.182130.00.950.053.26298000.0
25135201816053165349306.022975.04136.016823.05772.0782896918.00217634.31017112.378641jeromeID0.1062480.01.3SNAP1.85Other Nutrition Program0.6420.2580.1000.122890.00.750.252.851195000.0
25136201816061166141326.03788.0537.03057.01448.0163218914.17634647.36669911.580882lewisID0.150580.01.3SNAP1.85Other Nutrition Program0.5360.2700.1940.195170.00.790.223.01296000.0
25137201816073167340430.011316.02388.08753.03546.0425067621.10286340.51182515.905882owyheeID0.1341540.01.3SNAP1.85Other Nutrition Program0.6160.1730.2120.174530.00.690.313.28856000.0
25138201816021162143507.011394.01909.09200.04619.0460554116.75443250.20652211.748100boundaryID0.1291490.01.3SNAP1.85Other Nutrition Program0.5120.0700.4180.173470.00.60.43.48879000.0